SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 651–660 of 1718 papers

TitleStatusHype
Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic—0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified